Low-power and Lossy Networks (LLNs), e.g., sensor networks, have a myriad of applications, such as smart grid, smart cities, home and building automation, etc. Various challenges are presented with LLNs, such as lossy links, low bandwidth, battery operation, low memory and/or processing capability, etc. Large-scale IP smart object networks pose a number of technical challenges. For instance, the degree of density of such networks (such as Smart Grid networks with a large number of sensors and actuators, smart cities, or advanced metering infrastructure or “AMI” networks) may be extremely high: it is not rare for each node to see several hundreds of neighbors.
Traffic Engineering (TE) relates to the set of techniques, technologies, protocols, and algorithms used to make best use of network resources to meet specific service level agreements (SLAs) such as bounded latency, link utilization, etc., especially when such resources are relatively scarce. TE is a critical piece of LLNs (e.g., and the Internet of Things or IoT) due to the strict resource constraints that these networks must typically operate under. Built with link technologies that may only offer at most tens of Kbits/sec, the offered traffic load will typically be relatively high compared to the available network resources. The challenge is thus to design TE mechanisms that do not consume significant resources (e.g., control plane load, CPU processing, etc.) since they must operate over the same constrained networks as the data-plane traffic. This is especially challenging for IoT networks since they must operate under a very high scale (e.g., thousands or potentially millions of nodes).